Predictive Information Decomposition as a Tool to Quantify Emergent Dynamical Behaviors In Physiological Networks

• DOI: 10.48550/arxiv.2502.00945
• Publication Date: 2025-02-02
• Authors: Luca Faes; Gorana Mijatović; Laura Sparacino; Alberto Porta
• Keywords: FOS: Computer and information sciences; Applications (stat.AP); Statistics - Applications

Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological networks. Methods: Given network system mapped by vector random process, we compute predictive information (PI) between present past states dissect it into amounts unique, redundant synergistic shared each unit. Emergence is then quantified as prevalence over contribution. The implemented practice using autoregressive (VAR) models. Results: Validation simulated VAR processes documents that arise networks where multiple causal interactions coexist with internal dynamics. application to cardiovascular respiratory mapping beat-to-beat variability heart rate, arterial pressure respiration measured rest during postural stress reveals presence statistically significant net synergy, well its modulation sympathetic nervous activation. Conclusion: Causal emergence can be efficiently assessed decomposing PI systems via models applied series. approach evidences synergy/redundancy balance hallmark integrated short-term autonomic control Significance: Measures provide practical tool quantify mechanisms influence determine dynamic state neural across distinct physiopathological conditions.